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Engineering - Signals & Communication | Privacy-Preserving Machine Learning for Speech Processing

Privacy-Preserving Machine Learning for Speech Processing

Series: Springer Theses

Pathak, Manas A.

2013, XVIII, 142 p.

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  • Nominated as outstanding PhD thesis from Carnegie Mellon University
  • Develops an efficient computational framework, making it possible to create speech processing applications such as voice biometrics, mining and speech recognition that are privacy-preserving
  • Presents a technology solution, which would allow a user to utilize an IVR system without fear that the system could learn undesired information, such as gender or nationality, or be able to record and edit voice
This thesis discusses the privacy issues in speech-based applications, including biometric authentication, surveillance, and external speech processing services. Manas A. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification, and speech recognition.

The thesis introduces tools from cryptography and machine learning and current techniques for improving the efficiency and scalability of the presented solutions, as well as experiments with prototype implementations of the solutions for execution time and accuracy on standardized speech datasets. Using the framework proposed  may make it possible for a surveillance agency to listen for a known terrorist, without being able to hear conversation from non-targeted, innocent civilians.

Content Level » Research

Keywords » Homomorphic Encryption - Locality Sensitive Hashing - Secure Multiparty Computation - Speaker Identification - Speaker Verification - Speech Recognition

Related subjects » Energy Technology - Security and Cryptology - Signals & Communication

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